Evidence map›Paper›PMID 37293181›Full record

ArticleFrontiers in digital health2023

Analysis of Japanese consumers' attitudes toward the digital transformation of OTC medicine purchase behavior and eHealth literacy: an online survey for digital experience design.

Guyue Tang, Kairi Izumi, Megumi Izumisawa, Shinichi Koyama

Abstract read
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Article in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Guyue TangGraduate School of Comprehensive Human Sciences, University of Tsukuba, Ibaraki, Japan.
Kairi IzumiGraduate School of Comprehensive Human Sciences, University of Tsukuba, Ibaraki, Japan.
Megumi IzumisawaDepartment of Pharmacy, Nihon University, Chiba, Japan.
Shinichi KoyamaGraduate School of Comprehensive Human Sciences, University of Tsukuba, Ibaraki, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Since the enactment of the revised Pharmaceutical Affairs Act in Japan in 2009, self-medication practices have increased in the country. However, studies report that consumers pay little attention to the medication facts and risks indicated on the packages of over-the-counter (OTC) medicines, which could be a potential risk. Since the COVID-19 pandemic, the digital transformation of purchasing OTC medicines has progressed. As an appropriate design for the digital transformation is likely to improve consumers' literacy and them obtaining medical information, this study systematically examines Japanese consumers' attitudes toward the digital transformation of OTC medicine purchase behavior and its correlation to eHealth literacy, exploring an appropriate digital experience design in purchasing OTC medicine. Methods: Participants from the Greater Tokyo Area of Japan participated in an online survey. Consumers' current behavior and preferences in accessing OTC medicine, receiving medication guidance, and obtaining medical information were examined. eHealth literacy was assessed using the J-eHEALS. Descriptive statistics, text mining, and thematic analysis were conducted to answer research questions. Results: Over 89% of the respondents who had experience in purchasing OTC medicines preferred local pharmacies or stores rather than online purchasing, Conclusions: Japanese consumers are seeking a combination of conventional and digital behaviors for purchasing OTC medicine rather than opting for a particular method. Most consumers prefer purchasing and receiving instructions in-store while searching for additional decision-making information online. eHealth literacy is positively associated with digital behaviors of OTC medicine information acquisition but less associated with medicine purchases and selections. The hybrid digital experience design may enhance the OTC medicine purchase experience and reduce potential risks by providing appropriate information.

Indexed as

consumer behaviordigital healtheHealth literacyhuman factorsOTC medicineuser experience

Identifiers

PMID37293181
PMCPMC10244771

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.